Prerequisites

Overview

Hadoop MapReduce is a software framework for easily writing
applications which process vast amounts of data (multi-terabyte data-sets)
in-parallel on large clusters (thousands of nodes) of commodity
hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into
independent chunks which are processed by the map tasks in a
completely parallel manner. The framework sorts the outputs of the maps,
which are then input to the reduce tasks. Typically both the
input and the output of the job are stored in a file-system. The framework
takes care of scheduling tasks, monitoring them and re-executes the failed
tasks.

Typically the compute nodes and the storage nodes are the same, that is,
the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide)
are running on the same set of nodes. This configuration
allows the framework to effectively schedule tasks on the nodes where data
is already present, resulting in very high aggregate bandwidth across the
cluster.

The MapReduce framework consists of a single master
JobTracker and one slave TaskTracker per
cluster-node. The master is responsible for scheduling the jobs' component
tasks on the slaves, monitoring them and re-executing the failed tasks. The
slaves execute the tasks as directed by the master.

Minimally, applications specify the input/output locations and supply
map and reduce functions via implementations of
appropriate interfaces and/or abstract-classes. These, and other job
parameters, comprise the job configuration. The Hadoop
job client then submits the job (jar/executable etc.) and
configuration to the JobTracker which then assumes the
responsibility of distributing the software/configuration to the slaves,
scheduling tasks and monitoring them, providing status and diagnostic
information to the job-client.

Although the Hadoop framework is implemented in JavaTM,
MapReduce applications need not be written in Java.

Hadoop Streaming is a utility which allows users to create and run
jobs with any executables (e.g. shell utilities) as the mapper and/or
the reducer.

Inputs and Outputs

The MapReduce framework operates exclusively on
<key, value> pairs, that is, the framework views the
input to the job as a set of <key, value> pairs and
produces a set of <key, value> pairs as the output of
the job, conceivably of different types.

The key and value classes have to be
serializable by the framework and hence need to implement the
Writable
interface. Additionally, the key classes have to implement the
WritableComparable interface to facilitate sorting by the framework.

Applications can specify a comma separated list of paths which
would be present in the current working directory of the task
using the option -files. The -libjars
option allows applications to add jars to the classpaths of the maps
and reduces. The option -archives allows them to pass
comma separated list of archives as arguments. These archives are
unarchived and a link with name of the archive is created in
the current working directory of tasks. More
details about the command line options are available at
Commands Guide.

Users can specify a different symbolic name for
files and archives passed through -files and -archives option, using #.

For example,
hadoop jar hadoop-examples.jar wordcount
-files dir1/dict.txt#dict1,dir2/dict.txt#dict2
-archives mytar.tgz#tgzdir input output
Here, the files dir1/dict.txt and dir2/dict.txt can be accessed by
tasks using the symbolic names dict1 and dict2 respectively.
The archive mytar.tgz will be placed and unarchived into a
directory by the name "tgzdir".

Walk-through

The WordCount application is quite straight-forward.

The Mapper implementation (lines 14-26), via the
map method (lines 18-25), processes one line at a time,
as provided by the specified TextInputFormat (line 49).
It then splits the line into tokens separated by whitespaces, via the
StringTokenizer, and emits a key-value pair of
< <word>, 1>.

We'll learn more about the number of maps spawned for a given job, and
how to control them in a fine-grained manner, a bit later in the
tutorial.

WordCount also specifies a combiner (line
46). Hence, the output of each map is passed through the local combiner
(which is same as the Reducer as per the job
configuration) for local aggregation, after being sorted on the
keys.

The output of the first map:< Bye, 1>< Hello, 1>< World, 2>

The output of the second map:< Goodbye, 1>< Hadoop, 2>< Hello, 1>

The Reducer implementation (lines 28-36), via the
reduce method (lines 29-35) just sums up the values,
which are the occurence counts for each key (i.e. words in this example).

The run method specifies various facets of the job, such
as the input/output paths (passed via the command line), key/value
types, input/output formats etc., in the JobConf.
It then calls the JobClient.runJob (line 55) to submit the
and monitor its progress.

We'll learn more about JobConf, JobClient,
Tool and other interfaces and classes a bit later in the
tutorial.

MapReduce - User Interfaces

This section provides a reasonable amount of detail on every user-facing
aspect of the MapReduce framework. This should help users implement,
configure and tune their jobs in a fine-grained manner. However, please
note that the javadoc for each class/interface remains the most
comprehensive documentation available; this is only meant to be a tutorial.

Let us first take the Mapper and Reducer
interfaces. Applications typically implement them to provide the
map and reduce methods.

We will then discuss other core interfaces including
JobConf, JobClient, Partitioner,
OutputCollector, Reporter,
InputFormat, OutputFormat,
OutputCommitter and others.

Finally, we will wrap up by discussing some useful features of the
framework such as the DistributedCache,
IsolationRunner etc.

Payload

Applications typically implement the Mapper and
Reducer interfaces to provide the map and
reduce methods. These form the core of the job.

Mapper

Mapper maps input key/value pairs to a set of intermediate
key/value pairs.

Maps are the individual tasks that transform input records into
intermediate records. The transformed intermediate records do not need
to be of the same type as the input records. A given input pair may
map to zero or many output pairs.

The Hadoop MapReduce framework spawns one map task for each
InputSplit generated by the InputFormat for
the job.

Applications can use the Reporter to report
progress, set application-level status messages and update
Counters, or just indicate that they are alive.

All intermediate values associated with a given output key are
subsequently grouped by the framework, and passed to the
Reducer(s) to determine the final output. Users can
control the grouping by specifying a Comparator via
JobConf.setOutputKeyComparatorClass(Class).

The Mapper outputs are sorted and then
partitioned per Reducer. The total number of partitions is
the same as the number of reduce tasks for the job. Users can control
which keys (and hence records) go to which Reducer by
implementing a custom Partitioner.

Users can optionally specify a combiner, via
JobConf.setCombinerClass(Class), to perform local aggregation of
the intermediate outputs, which helps to cut down the amount of data
transferred from the Mapper to the Reducer.

The intermediate, sorted outputs are always stored in a simple
(key-len, key, value-len, value) format.
Applications can control if, and how, the
intermediate outputs are to be compressed and the
CompressionCodec to be used via the JobConf.

How Many Maps?

The number of maps is usually driven by the total size of the
inputs, that is, the total number of blocks of the input files.

The right level of parallelism for maps seems to be around 10-100
maps per-node, although it has been set up to 300 maps for very
cpu-light map tasks. Task setup takes awhile, so it is best if the
maps take at least a minute to execute.

Thus, if you expect 10TB of input data and have a blocksize of
128MB, you'll end up with 82,000 maps, unless
setNumMapTasks(int) (which only provides a hint to the framework)
is used to set it even higher.

Reducer

Reducer reduces a set of intermediate values which share a key to
a smaller set of values.

Applications can use the Reporter to report
progress, set application-level status messages and update
Counters, or just indicate that they are alive.

The output of the Reducer is not sorted.

How Many Reduces?

The right number of reduces seems to be 0.95 or
1.75 multiplied by (<no. of nodes> *
mapred.tasktracker.reduce.tasks.maximum).

With 0.95 all of the reduces can launch immediately
and start transfering map outputs as the maps finish. With
1.75 the faster nodes will finish their first round of
reduces and launch a second wave of reduces doing a much better job
of load balancing.

Increasing the number of reduces increases the framework overhead,
but increases load balancing and lowers the cost of failures.

The scaling factors above are slightly less than whole numbers to
reserve a few reduce slots in the framework for speculative-tasks and
failed tasks.

Reducer NONE

It is legal to set the number of reduce-tasks to zero if
no reduction is desired.

In this case the outputs of the map-tasks go directly to the
FileSystem, into the output path set by
setOutputPath(Path). The framework does not sort the
map-outputs before writing them out to the FileSystem.

Partitioner

Partitioner controls the partitioning of the keys of the
intermediate map-outputs. The key (or a subset of the key) is used to
derive the partition, typically by a hash function. The total
number of partitions is the same as the number of reduce tasks for the
job. Hence this controls which of the m reduce tasks the
intermediate key (and hence the record) is sent to for reduction.

Reporter

Reporter is a facility for MapReduce applications to report
progress, set application-level status messages and update
Counters.

Mapper and Reducer implementations can use
the Reporter to report progress or just indicate
that they are alive. In scenarios where the application takes a
significant amount of time to process individual key/value pairs,
this is crucial since the framework might assume that the task has
timed-out and kill that task. Another way to avoid this is to
set the configuration parameter mapred.task.timeout to a
high-enough value (or even set it to zero for no time-outs).

Applications can also update Counters using the
Reporter.

OutputCollector

OutputCollector is a generalization of the facility provided by
the MapReduce framework to collect data output by the
Mapper or the Reducer (either the
intermediate outputs or the output of the job).

Job Configuration

JobConf is the primary interface for a user to describe
a MapReduce job to the Hadoop framework for execution. The framework
tries to faithfully execute the job as described by JobConf,
however:

f
Some configuration parameters may have been marked as
final by administrators and hence cannot be altered.

While some job parameters are straight-forward to set (e.g.
setNumReduceTasks(int)), other parameters interact subtly with
the rest of the framework and/or job configuration and are
more complex to set (e.g.
setNumMapTasks(int)).

Of course, users can use
set(String, String)/get(String, String)
to set/get arbitrary parameters needed by applications. However, use the
DistributedCache for large amounts of (read-only) data.

Task Execution & Environment

The TaskTracker executes the Mapper/
Reducertask as a child process in a separate jvm.

The child-task inherits the environment of the parent
TaskTracker. The user can specify additional options to the
child-jvm via the mapred.{map|reduce}.child.java.opts
configuration parameter in the JobConf such as non-standard
paths for the run-time linker to search shared libraries via
-Djava.library.path=<> etc. If the
mapred.{map|reduce}.child.java.opts parameters contains the
symbol @taskid@ it is interpolated with value of
taskid of the MapReduce task.

Here is an example with multiple arguments and substitutions,
showing jvm GC logging, and start of a passwordless JVM JMX agent so that
it can connect with jconsole and the likes to watch child memory,
threads and get thread dumps. It also sets the maximum heap-size of the
map and reduce child jvm to 512MB & 1024MB respectively. It also
adds an additional path to the java.library.path of the
child-jvm.

Memory Management

Users/admins can also specify the maximum virtual memory
of the launched child-task, and any sub-process it launches
recursively, using mapred.{map|reduce}.child.ulimit. Note
that the value set here is a per process limit.
The value for mapred.{map|reduce}.child.ulimit should be
specified in kilo bytes (KB). And also the value must be greater than
or equal to the -Xmx passed to JavaVM, else the VM might not start.

The memory available to some parts of the framework is also
configurable. In map and reduce tasks, performance may be influenced
by adjusting parameters influencing the concurrency of operations and
the frequency with which data will hit disk. Monitoring the filesystem
counters for a job- particularly relative to byte counts from the map
and into the reduce- is invaluable to the tuning of these
parameters.

Users can choose to override default limits of Virtual Memory and RAM
enforced by the task tracker, if memory management is enabled.
Users can set the following parameter per job:

Name

Type

Description

mapred.task.maxvmem

int

A number, in bytes, that represents the maximum Virtual Memory
task-limit for each task of the job. A task will be killed if
it consumes more Virtual Memory than this number.

mapred.task.maxpmem

int

A number, in bytes, that represents the maximum RAM task-limit
for each task of the job. This number can be optionally used by
Schedulers to prevent over-scheduling of tasks on a node based
on RAM needs.

Map Parameters

A record emitted from a map will be serialized into a buffer and
metadata will be stored into accounting buffers. As described in the
following options, when either the serialization buffer or the
metadata exceed a threshold, the contents of the buffers will be
sorted and written to disk in the background while the map continues
to output records. If either buffer fills completely while the spill
is in progress, the map thread will block. When the map is finished,
any remaining records are written to disk and all on-disk segments
are merged into a single file. Minimizing the number of spills to
disk can decrease map time, but a larger buffer also decreases the
memory available to the mapper.

Name

Type

Description

io.sort.mb

int

The cumulative size of the serialization and accounting
buffers storing records emitted from the map, in megabytes.

io.sort.record.percent

float

The ratio of serialization to accounting space can be
adjusted. Each serialized record requires 16 bytes of
accounting information in addition to its serialized size to
effect the sort. This percentage of space allocated from
io.sort.mb affects the probability of a spill to
disk being caused by either exhaustion of the serialization
buffer or the accounting space. Clearly, for a map outputting
small records, a higher value than the default will likely
decrease the number of spills to disk.

io.sort.spill.percent

float

This is the threshold for the accounting and serialization
buffers. When this percentage of either buffer has filled,
their contents will be spilled to disk in the background. Let
io.sort.record.percent be r,
io.sort.mb be x, and this value be
q. The maximum number of records collected before the
collection thread will spill is r * x * q * 2^16.
Note that a higher value may decrease the number of- or even
eliminate- merges, but will also increase the probability of
the map task getting blocked. The lowest average map times are
usually obtained by accurately estimating the size of the map
output and preventing multiple spills.

Other notes

If either spill threshold is exceeded while a spill is in
progress, collection will continue until the spill is finished.
For example, if io.sort.buffer.spill.percent is set
to 0.33, and the remainder of the buffer is filled while the spill
runs, the next spill will include all the collected records, or
0.66 of the buffer, and will not generate additional spills. In
other words, the thresholds are defining triggers, not
blocking.

A record larger than the serialization buffer will first
trigger a spill, then be spilled to a separate file. It is
undefined whether or not this record will first pass through the
combiner.

Shuffle/Reduce Parameters

As described previously, each reduce fetches the output assigned
to it by the Partitioner via HTTP into memory and periodically
merges these outputs to disk. If intermediate compression of map
outputs is turned on, each output is decompressed into memory. The
following options affect the frequency of these merges to disk prior
to the reduce and the memory allocated to map output during the
reduce.

Name

Type

Description

io.sort.factor

int

Specifies the number of segments on disk to be merged at
the same time. It limits the number of open files and
compression codecs during the merge. If the number of files
exceeds this limit, the merge will proceed in several passes.
Though this limit also applies to the map, most jobs should be
configured so that hitting this limit is unlikely
there.

mapred.inmem.merge.threshold

int

The number of sorted map outputs fetched into memory
before being merged to disk. Like the spill thresholds in the
preceding note, this is not defining a unit of partition, but
a trigger. In practice, this is usually set very high (1000)
or disabled (0), since merging in-memory segments is often
less expensive than merging from disk (see notes following
this table). This threshold influences only the frequency of
in-memory merges during the shuffle.

mapred.job.shuffle.merge.percent

float

The memory threshold for fetched map outputs before an
in-memory merge is started, expressed as a percentage of
memory allocated to storing map outputs in memory. Since map
outputs that can't fit in memory can be stalled, setting this
high may decrease parallelism between the fetch and merge.
Conversely, values as high as 1.0 have been effective for
reduces whose input can fit entirely in memory. This parameter
influences only the frequency of in-memory merges during the
shuffle.

mapred.job.shuffle.input.buffer.percent

float

The percentage of memory- relative to the maximum heapsize
as typically specified in mapred.reduce.child.java.opts-
that can be allocated to storing map outputs during the
shuffle. Though some memory should be set aside for the
framework, in general it is advantageous to set this high
enough to store large and numerous map outputs.

mapred.job.reduce.input.buffer.percent

float

The percentage of memory relative to the maximum heapsize
in which map outputs may be retained during the reduce. When
the reduce begins, map outputs will be merged to disk until
those that remain are under the resource limit this defines.
By default, all map outputs are merged to disk before the
reduce begins to maximize the memory available to the reduce.
For less memory-intensive reduces, this should be increased to
avoid trips to disk.

Other notes

If a map output is larger than 25 percent of the memory
allocated to copying map outputs, it will be written directly to
disk without first staging through memory.

When running with a combiner, the reasoning about high merge
thresholds and large buffers may not hold. For merges started
before all map outputs have been fetched, the combiner is run
while spilling to disk. In some cases, one can obtain better
reduce times by spending resources combining map outputs- making
disk spills small and parallelizing spilling and fetching- rather
than aggressively increasing buffer sizes.

When merging in-memory map outputs to disk to begin the
reduce, if an intermediate merge is necessary because there are
segments to spill and at least io.sort.factor
segments already on disk, the in-memory map outputs will be part
of the intermediate merge.

Directory Structure

The task tracker has local directory,
${mapred.local.dir}/taskTracker/ to create localized
cache and localized job. It can define multiple local directories
(spanning multiple disks) and then each filename is assigned to a
semi-random local directory. When the job starts, task tracker
creates a localized job directory relative to the local directory
specified in the configuration. Thus the task tracker directory
structure looks as following:

${mapred.local.dir}/taskTracker/distcache/ :
The public distributed cache for the jobs of all users. This directory
holds the localized public distributed cache. Thus localized public
distributed cache is shared among all the tasks and jobs of all users.

${mapred.local.dir}/taskTracker/$user/distcache/ :
The private distributed cache for the jobs of the specific user. This
directory holds the localized private distributed cache. Thus localized
private distributed cache is shared among all the tasks and jobs of the
specific user only. It is not accessible to jobs of other users.

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/work/
: The job-specific shared directory. The tasks can use this space as
scratch space and share files among them. This directory is exposed
to the users through the configuration property
job.local.dir. The directory can accessed through
the API
JobConf.getJobLocalDir(). It is available as System property also.
So, users (streaming etc.) can call
System.getProperty("job.local.dir") to access the
directory.

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/jars/
: The jars directory, which has the job jar file and expanded jar.
The job.jar is the application's jar file that is
automatically distributed to each machine. It is expanded in jars
directory before the tasks for the job start. The job.jar location
is accessible to the application through the api
JobConf.getJar() . To access the unjarred directory,
JobConf.getJar().getParent() can be called.

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/job.xml
: The job.xml file, the generic job configuration, localized for
the job.

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/$taskid
: The task directory for each task attempt. Each task directory
again has the following structure :

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/$taskid/job.xml
: A job.xml file, task localized job configuration, Task localization
means that properties have been set that are specific to
this particular task within the job. The properties localized for
each task are described below.

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/$taskid/output
: A directory for intermediate output files. This contains the
temporary map reduce data generated by the framework
such as map output files etc.

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/$taskid/work
: The current working directory of the task.
With jvm reuse enabled for tasks, this
directory will be the directory on which the jvm has started

${mapred.local.dir}/taskTracker/$user/jobcache/$jobid/$taskid/work/tmp
: The temporary directory for the task.
(User can specify the property mapred.child.tmp to set
the value of temporary directory for map and reduce tasks. This
defaults to ./tmp. If the value is not an absolute path,
it is prepended with task's working directory. Otherwise, it is
directly assigned. The directory will be created if it doesn't exist.
Then, the child java tasks are executed with option
-Djava.io.tmpdir='the absolute path of the tmp dir'.
Pipes and streaming are set with environment variable,
TMPDIR='the absolute path of the tmp dir'). This
directory is created, if mapred.child.tmp has the value
./tmp

Task JVM Reuse

Jobs can enable task JVMs to be reused by specifying the job
configuration mapred.job.reuse.jvm.num.tasks. If the
value is 1 (the default), then JVMs are not reused
(i.e. 1 task per JVM). If it is -1, there is no limit to the number
of tasks a JVM can run (of the same job). One can also specify some
value greater than 1 using the api
JobConf.setNumTasksToExecutePerJvm(int)

Configured Parameters

The following properties are localized in the job configuration
for each task's execution:

Name

Type

Description

mapred.job.id

String

The job id

mapred.jar

String

job.jar location in job directory

job.local.dir

String

The job specific shared scratch space

mapred.tip.id

String

The task id

mapred.task.id

String

The task attempt id

mapred.task.is.map

boolean

Is this a map task

mapred.task.partition

int

The id of the task within the job

map.input.file

String

The filename that the map is reading from

map.input.start

long

The offset of the start of the map input split

map.input.length

long

The number of bytes in the map input split

mapred.work.output.dir

String

The task's temporary output directory

Note:
During the execution of a streaming job, the names of the "mapred" parameters are transformed.
The dots ( . ) become underscores ( _ ).
For example, mapred.job.id becomes mapred_job_id and mapred.jar becomes mapred_jar.
To get the values in a streaming job's mapper/reducer use the parameter names with the underscores.

Task Logs

The standard output (stdout) and error (stderr) streams of the task
are read by the TaskTracker and logged to
${HADOOP_LOG_DIR}/userlogs

Distributing Libraries

The DistributedCache can also be used
to distribute both jars and native libraries for use in the map
and/or reduce tasks. The child-jvm always has its
current working directory added to the
java.library.path and LD_LIBRARY_PATH.
And hence the cached libraries can be loaded via
System.loadLibrary or
System.load. More details on how to load shared libraries through
distributed cache are documented at
native_libraries.html

Job Submission and Monitoring

JobClient is the primary interface by which user-job interacts
with the JobTracker.

JobClient provides facilities to submit jobs, track their
progress, access component-tasks' reports and logs, get the MapReduce
cluster's status information and so on.

The job submission process involves:

Checking the input and output specifications of the job.

Computing the InputSplit values for the job.

Setting up the requisite accounting information for the
DistributedCache of the job, if necessary.

Copying the job's jar and configuration to the MapReduce system
directory on the FileSystem.

Submitting the job to the JobTracker and optionally
monitoring it's status.

Job history files are also logged to user specified directory
hadoop.job.history.user.location
which defaults to job output directory. The files are stored in
"_logs/history/" in the specified directory. Hence, by default they
will be in mapred.output.dir/_logs/history. User can stop
logging by giving the value none for
hadoop.job.history.user.location

User can view the history logs summary in specified directory
using the following command $ bin/hadoop job -history output-dir
This command will print job details, failed and killed tip
details.
More details about the job such as successful tasks and
task attempts made for each task can be viewed using the
following command $ bin/hadoop job -history all output-dir

User can use
OutputLogFilter
to filter log files from the output directory listing.

Normally the user creates the application, describes various facets
of the job via JobConf, and then uses the
JobClient to submit the job and monitor its progress.

Job Authorization

Job level authorization and queue level authorization are enabled
on the cluster, if the configuration
mapred.acls.enabled is set to
true. When enabled, access control checks are done by (a) the
JobTracker before allowing users to submit jobs to queues and
administering these jobs and (b) by the JobTracker and the TaskTracker
before allowing users to view job details or to modify a job using
MapReduce APIs, CLI or web user interfaces.

A job submitter can specify access control lists for viewing or
modifying a job via the configuration properties
mapreduce.job.acl-view-job and
mapreduce.job.acl-modify-job respectively. By default,
nobody is given access in these properties.

However, irrespective of the job ACLs configured, a job's owner,
the superuser and cluster administrators
(mapreduce.cluster.administrators) and queue
administrators of the queue to which the job was submitted to
(mapred.queue.queue-name.acl-administer-jobs) always
have access to view and modify a job.

A job view ACL authorizes users against the configured
mapreduce.job.acl-view-job before returning possibly
sensitive information about a job, like:

job level counters

task level counters

tasks's diagnostic information

task logs displayed on the TaskTracker web UI

job.xml showed by the JobTracker's web UI

Other information about a job, like its status and its profile,
is accessible to all users, without requiring authorization.

A job modification ACL authorizes users against the configured
mapreduce.job.acl-modify-job before allowing
modifications to jobs, like:

killing a job

killing/failing a task of a job

setting the priority of a job

These operations are also permitted by the queue level ACL,
"mapred.queue.queue-name.acl-administer-jobs", configured via
mapred-queue-acls.xml. The caller will be able to do the operation
if he/she is part of either queue admins ACL or job modification ACL.

The format of a job level ACL is the same as the format for a
queue level ACL as defined in the
Cluster Setup documentation.

Job Control

Users may need to chain MapReduce jobs to accomplish complex
tasks which cannot be done via a single MapReduce job. This is fairly
easy since the output of the job typically goes to distributed
file-system, and the output, in turn, can be used as the input for the
next job.

However, this also means that the onus on ensuring jobs are
complete (success/failure) lies squarely on the clients. In such
cases, the various job-control options are:

Job Credentials

In a secure cluster, the user is authenticated via Kerberos'
kinit command. Because of scalability concerns, we don't push
the client's Kerberos' tickets in MapReduce jobs. Instead, we
acquire delegation tokens from each HDFS NameNode that the job
will use and store them in the job as part of job submission.
The delegation tokens are automatically obtained
for the HDFS that holds the staging directories, where the job
job files are written, and any HDFS systems referenced by
FileInputFormats, FileOutputFormats, DistCp, and the
distributed cache.
Other applications require to set the configuration
"mapreduce.job.hdfs-servers" for all NameNodes that tasks might
need to talk during the job execution. This is a comma separated
list of file system names, such as "hdfs://nn1/,hdfs://nn2/".
These tokens are passed to the JobTracker
as part of the job submission as Credentials.

Similar to HDFS delegation tokens, we also have MapReduce delegation tokens. The
MapReduce tokens are provided so that tasks can spawn jobs if they wish to. The tasks authenticate
to the JobTracker via the MapReduce delegation tokens. The delegation token can
be obtained via the API in
JobClient.getDelegationToken. The obtained token must then be pushed onto the
credentials that is there in the JobConf used for job submission. The API
Credentials.addToken
can be used for this.

The credentials are sent to the JobTracker as part of the job submission process.
The JobTracker persists the tokens and secrets in its filesystem (typically HDFS)
in a file within mapred.system.dir/JOBID. The TaskTracker localizes the file as part
job localization. Tasks see an environment variable called
HADOOP_TOKEN_FILE_LOCATION and the framework sets this to point to the
localized file. In order to launch jobs from tasks or for doing any HDFS operation,
tasks must set the configuration "mapreduce.job.credentials.binary" to point to
this token file.

The HDFS delegation tokens passed to the JobTracker during job submission are
are cancelled by the JobTracker when the job completes. This is the default behavior
unless mapreduce.job.complete.cancel.delegation.tokens is set to false in the
JobConf. For jobs whose tasks in turn spawns jobs, this should be set to false.
Applications sharing JobConf objects between multiple jobs on the JobClient side
should look at setting mapreduce.job.complete.cancel.delegation.tokens to false.
This is because the Credentials object within the JobConf will then be shared.
All jobs will end up sharing the same tokens, and hence the tokens should not be
canceled when the jobs in the sequence finish.

For applications written using the old MapReduce API, the Mapper/Reducer classes
need to implement
JobConfigurable in order to get access to the credentials in the tasks.
A reference to the JobConf passed in the
JobConfigurable.configure should be stored. In the new MapReduce API,
a similar thing can be done in the
Mapper.setup
method.
The api
JobConf.getCredentials() or the api JobContext.getCredentials()
should be used to get the credentials reference (depending
on whether the new MapReduce API or the old MapReduce API is used).
Tasks can access the secrets using the APIs in Credentials

Job Input

Split-up the input file(s) into logical InputSplit
instances, each of which is then assigned to an individual
Mapper.

Provide the RecordReader implementation used to
glean input records from the logical InputSplit for
processing by the Mapper.

The default behavior of file-based InputFormat
implementations, typically sub-classes of
FileInputFormat, is to split the input into logicalInputSplit instances based on the total size, in bytes, of
the input files. However, the FileSystem blocksize of the
input files is treated as an upper bound for input splits. A lower bound
on the split size can be set via mapred.min.split.size.

Clearly, logical splits based on input-size is insufficient for many
applications since record boundaries must be respected. In such cases,
the application should implement a RecordReader, who is
responsible for respecting record-boundaries and presents a
record-oriented view of the logical InputSplit to the
individual task.

If TextInputFormat is the InputFormat for a
given job, the framework detects input-files with the .gz
extensions and automatically decompresses them using the
appropriate CompressionCodec. However, it must be noted that
compressed files with the above extensions cannot be split and
each compressed file is processed in its entirety by a single mapper.

InputSplit

InputSplit represents the data to be processed by an individual
Mapper.

Typically InputSplit presents a byte-oriented view of
the input, and it is the responsibility of RecordReader
to process and present a record-oriented view.

FileSplit is the default InputSplit. It sets
map.input.file to the path of the input file for the
logical split.

RecordReader

Typically the RecordReader converts the byte-oriented
view of the input, provided by the InputSplit, and
presents a record-oriented to the Mapper implementations
for processing. RecordReader thus assumes the
responsibility of processing record boundaries and presents the tasks
with keys and values.

OutputCommitter

Setup the job during initialization. For example, create
the temporary output directory for the job during the
initialization of the job.
Job setup is done by a separate task when the job is
in PREP state and after initializing tasks. Once the setup task
completes, the job will be moved to RUNNING state.

Cleanup the job after the job completion. For example, remove the
temporary output directory after the job completion.
Job cleanup is done by a separate task at the end of the job.
Job is declared SUCCEDED/FAILED/KILLED after the cleanup
task completes.

Setup the task temporary output.
Task setup is done as part of the same task, during task initialization.

Check whether a task needs a commit. This is to avoid the commit
procedure if a task does not need commit.

Commit of the task output.
Once task is done, the task will commit it's output if required.

Discard the task commit.
If the task has been failed/killed, the output will be cleaned-up.
If task could not cleanup (in exception block), a separate task
will be launched with same attempt-id to do the cleanup.

FileOutputCommitter is the default
OutputCommitter. Job setup/cleanup tasks occupy
map or reduce slots, whichever is free on the TaskTracker. And
JobCleanup task, TaskCleanup tasks and JobSetup task have the highest
priority, and in that order.

Task Side-Effect Files

In some applications, component tasks need to create and/or write to
side-files, which differ from the actual job-output files.

In such cases there could be issues with two instances of the same
Mapper or Reducer running simultaneously (for
example, speculative tasks) trying to open and/or write to the same
file (path) on the FileSystem. Hence the
application-writer will have to pick unique names per task-attempt
(using the attemptid, say attempt_200709221812_0001_m_000000_0),
not just per task.

To avoid these issues the MapReduce framework, when the
OutputCommitter is FileOutputCommitter,
maintains a special
${mapred.output.dir}/_temporary/_${taskid} sub-directory
accessible via ${mapred.work.output.dir}
for each task-attempt on the FileSystem where the output
of the task-attempt is stored. On successful completion of the
task-attempt, the files in the
${mapred.output.dir}/_temporary/_${taskid} (only)
are promoted to ${mapred.output.dir}. Of course,
the framework discards the sub-directory of unsuccessful task-attempts.
This process is completely transparent to the application.

The application-writer can take advantage of this feature by
creating any side-files required in ${mapred.work.output.dir}
during execution of a task via
FileOutputFormat.getWorkOutputPath(), and the framework will promote them
similarly for succesful task-attempts, thus eliminating the need to
pick unique paths per task-attempt.

Note: The value of ${mapred.work.output.dir} during
execution of a particular task-attempt is actually
${mapred.output.dir}/_temporary/_{$taskid}, and this value is
set by the MapReduce framework. So, just create any side-files in the
path returned by
FileOutputFormat.getWorkOutputPath() from MapReduce
task to take advantage of this feature.

The entire discussion holds true for maps of jobs with
reducer=NONE (i.e. 0 reduces) since output of the map, in that case,
goes directly to HDFS.

RecordWriter

Other Useful Features

Submitting Jobs to Queues

Users submit jobs to Queues. Queues, as collection of jobs,
allow the system to provide specific functionality. For example,
queues use ACLs to control which users
who can submit jobs to them. Queues are expected to be primarily
used by Hadoop Schedulers.

Hadoop comes configured with a single mandatory queue, called
'default'. Queue names are defined in the
mapred.queue.names property of the Hadoop site
configuration. Some job schedulers, such as the
Capacity Scheduler,
support multiple queues.

A job defines the queue it needs to be submitted to through the
mapred.job.queue.name property, or through the
setQueueName(String)
API. Setting the queue name is optional. If a job is submitted
without an associated queue name, it is submitted to the 'default'
queue.

Counters

Counters represent global counters, defined either by
the MapReduce framework or applications. Each Counter can
be of any Enum type. Counters of a particular
Enum are bunched into groups of type
Counters.Group.

DistributedCache

DistributedCache is a facility provided by the
MapReduce framework to cache files (text, archives, jars and so on)
needed by applications.

Applications specify the files to be cached via urls (hdfs://)
in the JobConf. The DistributedCache
assumes that the files specified via hdfs:// urls are already present
on the FileSystem.

The framework will copy the necessary files to the slave node
before any tasks for the job are executed on that node. Its
efficiency stems from the fact that the files are only copied once
per job and the ability to cache archives which are un-archived on
the slaves.

DistributedCache tracks the modification timestamps of
the cached files. Clearly the cache files should not be modified by
the application or externally while the job is executing.

DistributedCache can be used to distribute simple,
read-only data/text files and more complex types such as archives and
jars. Archives (zip, tar, tgz and tar.gz files) are
un-archived at the slave nodes. Files
have execution permissions set.

Optionally users can also direct the DistributedCache
to symlink the cached file(s) into the current working
directory of the task via the
DistributedCache.createSymlink(Configuration) api. Or by setting
the configuration property mapred.create.symlink
as yes. The DistributedCache will use the
fragment of the URI as the name of the symlink.
For example, the URI
hdfs://namenode:port/lib.so.1#lib.so
will have the symlink name as lib.so in task's cwd
for the file lib.so.1 in distributed cache.

The DistributedCache can also be used as a
rudimentary software distribution mechanism for use in the
map and/or reduce tasks. It can be used to distribute both
jars and native libraries. The
DistributedCache.addArchiveToClassPath(Path, Configuration) or
DistributedCache.addFileToClassPath(Path, Configuration) api
can be used to cache files/jars and also add them to the
classpath of child-jvm. The same can be done by setting
the configuration properties
mapred.job.classpath.{files|archives}. Similarly the
cached files that are symlinked into the working directory of the
task can be used to distribute native libraries and load them.

Private and Public DistributedCache Files

DistributedCache files can be private or public, that
determines how they can be shared on the slave nodes.

"Private" DistributedCache files are cached in a local
directory private to the user whose jobs need these
files. These files are shared by all
tasks and jobs of the specific user only and cannot be accessed by
jobs of other users on the slaves. A DistributedCache file becomes private by
virtue of its permissions on the file system where the files
are uploaded, typically HDFS. If the file has no world readable
access, or if the directory path leading to the file has no
world executable access for lookup, then the file becomes private.

"Public" DistributedCache files are cached in a global
directory and the file access is setup such that they are
publicly visible to all users. These files can be shared by
tasks and jobs of all users on the slaves.
A DistributedCache file becomes public by virtue of its permissions
on the file system where the files are uploaded, typically HDFS.
If the file has world readable access, AND if the directory
path leading to the file has world executable access for lookup,
then the file becomes public. In other words, if the user intends
to make a file publicly available to all users, the file permissions
must be set to be world readable, and the directory permissions
on the path leading to the file must be world executable.

IsolationRunner

To use the IsolationRunner, first set
keep.failed.task.files to true
(also see keep.task.files.pattern).

Next, go to the node on which the failed task ran and go to the
TaskTracker's local directory and run the
IsolationRunner:$ cd <local path>/taskTracker/${taskid}/work
$ bin/hadoop org.apache.hadoop.mapred.IsolationRunner ../job.xml

IsolationRunner will run the failed task in a single
jvm, which can be in the debugger, over precisely the same input.

Note that currently IsolationRunner will only re-run map tasks.

Profiling

Profiling is a utility to get a representative (2 or 3) sample
of built-in java profiler for a sample of maps and reduces.

User can specify whether the system should collect profiler
information for some of the tasks in the job by setting the
configuration property mapred.task.profile. The
value can be set using the api
JobConf.setProfileEnabled(boolean). If the value is set
true, the task profiling is enabled. The profiler
information is stored in the user log directory. By default,
profiling is not enabled for the job.

Once user configures that profiling is needed, she/he can use
the configuration property
mapred.task.profile.{maps|reduces} to set the ranges
of MapReduce tasks to profile. The value can be set using the api
JobConf.setProfileTaskRange(boolean,String).
By default, the specified range is 0-2.

User can also specify the profiler configuration arguments by
setting the configuration property
mapred.task.profile.params. The value can be specified
using the api
JobConf.setProfileParams(String). If the string contains a
%s, it will be replaced with the name of the profiling
output file when the task runs. These parameters are passed to the
task child JVM on the command line. The default value for
the profiling parameters is
-agentlib:hprof=cpu=samples,heap=sites,force=n,thread=y,verbose=n,file=%s

Debugging

The MapReduce framework provides a facility to run user-provided
scripts for debugging. When a MapReduce task fails, a user can run
a debug script, to process task logs for example. The script is
given access to the task's stdout and stderr outputs, syslog and
jobconf. The output from the debug script's stdout and stderr is
displayed on the console diagnostics and also as part of the
job UI.

In the following sections we discuss how to submit a debug script
with a job. The script file needs to be distributed and submitted to
the framework.

How to distribute the script file:

The user needs to use
DistributedCache
to distribute and symlink the script file.

How to submit the script:

A quick way to submit the debug script is to set values for the
properties mapred.map.task.debug.script and
mapred.reduce.task.debug.script, for debugging map and
reduce tasks respectively. These properties can also be set by using APIs
JobConf.setMapDebugScript(String) and
JobConf.setReduceDebugScript(String) . In streaming mode, a debug
script can be submitted with the command-line options
-mapdebug and -reducedebug, for debugging
map and reduce tasks respectively.

The arguments to the script are the task's stdout, stderr,
syslog and jobconf files. The debug command, run on the node where
the MapReduce task failed, is: $script $stdout $stderr $syslog $jobconf

Pipes programs have the c++ program name as a fifth argument
for the command. Thus for the pipes programs the command is $script $stdout $stderr $syslog $jobconf $program

Default Behavior:

For pipes, a default script is run to process core dumps under
gdb, prints stack trace and gives info about running threads.

JobControl

JobControl is a utility which encapsulates a set of MapReduce jobs
and their dependencies.

Data Compression

Hadoop MapReduce provides facilities for the application-writer to
specify compression for both intermediate map-outputs and the
job-outputs i.e. output of the reduces. It also comes bundled with
CompressionCodec implementation for the
zlib compression
algorithm. The gzip file format is also
supported.

Hadoop also provides native implementations of the above compression
codecs for reasons of both performance (zlib) and non-availability of
Java libraries. More details on their usage and availability are
available here.

Skipping Bad Records

Hadoop provides an option where a certain set of bad input
records can be skipped when processing map inputs. Applications
can control this feature through the
SkipBadRecords class.

This feature can be used when map tasks crash deterministically
on certain input. This usually happens due to bugs in the
map function. Usually, the user would have to fix these bugs.
This is, however, not possible sometimes. The bug may be in third
party libraries, for example, for which the source code is not
available. In such cases, the task never completes successfully even
after multiple attempts, and the job fails. With this feature, only
a small portion of data surrounding the
bad records is lost, which may be acceptable for some applications
(those performing statistical analysis on very large data, for
example).

The number of records skipped depends on how frequently the
processed record counter is incremented by the application.
It is recommended that this counter be incremented after every
record is processed. This may not be possible in some applications
that typically batch their processing. In such cases, the framework
may skip additional records surrounding the bad record. Users can
control the number of skipped records through
SkipBadRecords.setMapperMaxSkipRecords(Configuration, long) and
SkipBadRecords.setReducerMaxSkipGroups(Configuration, long).
The framework tries to narrow the range of skipped records using a
binary search-like approach. The skipped range is divided into two
halves and only one half gets executed. On subsequent
failures, the framework figures out which half contains
bad records. A task will be re-executed till the
acceptable skipped value is met or all task attempts are exhausted.
To increase the number of task attempts, use
JobConf.setMaxMapAttempts(int) and
JobConf.setMaxReduceAttempts(int).